The present invention relates to a technique for detecting semantically significant events in a video sequence.
There has been a dramatic increase in the quantity of video available to the public. This trend is expected to continue or accelerate in the future with the convergence of personal computers and digital television. To improve the value of this information to users, tools are needed to help a user navigate through the available video information and find content that is relevant. For “consumer” users, such tools should be easy to understand, easy to use, and should provide reliable and predictable behavior.
Generally, there are three categories of known content-based video indexing and retrieval systems. A first category includes methods directed to the syntactic structure of video. This category includes methods of shot boundary detection, key frame extraction, shot clustering, tables of contents creation, video summarizing, and video skimming. These methods are generally computationally conservative and produce relatively reliable results. However, the results may not be semantically relevant since the methods do not attempt to model or estimate the meaning of the video content. As a result, searching or browsing video may be frustrating for users seeking video content of particular interest.
A second category of video indexing and retrieval systems attempts to classify video sequences into categories, such as, news, sports, action movies, close-ups, or crowds. These classifications may facilitate browsing video sequences at a coarse level but are of limited usefulness in aiding the user to find content of interest. Users often express the object of their searches in terms of labels with more exact meanings, such as, keywords describing objects, actions, or events. Video content analysis at a finer level than available with most classification systems is desirable to more effectively aid users to find content of interest.
The third category of techniques for analyzing video content applies rules relating the content to features of a specific video domain or content subject area. For example, methods have been proposed to detect events in football games, soccer games, baseball games and basketball games. The events detected by these methods are likely to be semantically relevant to users, but these methods are heavily dependent on the specific artifacts related to the particular domain, such as editing patterns in broadcast programs. This makes it difficult to extend these methods to more general analysis of video from a broad variety of domains.
What is desired, therefore, is a generic system for video analysis which reliably detects semantically significant events in a video.